tokenization_chatglm.py

import regex as re
import base64
import os
import tiktoken
from typing import List, Optional, Union, Dict
from transformers import PreTrainedTokenizer
from transformers.utils import PaddingStrategy
from transformers.tokenization_utils_base import EncodedInput, BatchEncoding


class ChatGLM4Tokenizer(PreTrainedTokenizer):
 vocab_files_names = {"vocab_file": "tokenizer.model"}
 model_input_names = ["input_ids", "attention_mask", "position_ids"]

 def __init__(
 self,
 vocab_file,
 clean_up_tokenization_spaces=False,
 **kwargs
 ):
 self.name = "GLM4Tokenizer"
 self.vocab_file = vocab_file
 pat_str = "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+"
 self.pat_str = re.compile(pat_str)

 mergeable_ranks = {}
 with open(vocab_file) as f:
 for line in f:
 token, rank = line.strip().split()
 rank = int(rank)
 token = base64.b64decode(token)
 mergeable_ranks[token] = rank

 self.mergeable_ranks = mergeable_ranks

 self.tokenizer = tiktoken.Encoding(
 name="my_tokenizer",
 pat_str=pat_str,
 mergeable_ranks=mergeable_ranks,
 special_tokens={}
 )
 self.decoder = {rank: token for token, rank in mergeable_ranks.items()}
 self.n_words = len(self.decoder)

 super().__init__(
 clean_up_tokenization_spaces=clean_up_tokenization_spaces,
 **kwargs
 )

 @property
 def vocab_size(self):
 return self.n_words

 def get_vocab(self):
 """ Returns vocab as a dict """
 vocab = {self._convert_id_to_token(i): i for i in range(self.vocab_size)}
 vocab.update(self.added_tokens_encoder)
 return vocab

 def convert_tokens_to_string(self, tokens: List[Union[bytes, str, int]]) -> str:
 """
 Converts a sequence of tokens in a single string.
 """
 text = ""
 temp = b""
 for t in tokens:
 if isinstance(t, int):
 t = chr(t)
 if isinstance(t, str):
 if temp:
 text += temp.decode("utf-8", errors="replace")
 elif isinstance(t, bytes):
 temp += t
 else:
 raise TypeError("token should only be of type int, bytes or str")
 if temp:
 text += temp.decode("utf-8", errors="replace")
 return text

 def _tokenize(self, text, **kwargs):
 tokens = []
 ids = self.tokenizer.encode(text)
 for t in ids:
 tokens.append(self.decoder[t])
 return tokens

 def _convert_token_to_id(self, token):
 """ Converts a token (str) in an id using the vocab. """
 return self.mergeable_ranks[token]

 def _convert_id_to_token(self, index):
 """Converts an index (integer) in a token (str) using the vocab."""
 return self.decoder.get(index, "")

 def save_vocabulary(self, save_directory, filename_prefix=None):
 """
 Save the vocabulary and special tokens file to a directory.

 Args:
 save_directory (`str`):
 The directory in which to save the vocabulary.
 filename_prefix (`str`, *optional*):
 An optional prefix to add to the named of the saved files.

 Returns:
 `Tuple(str)`: Paths to the files saved.
 """
 if os.path.isdir(save_directory):
 vocab_file = os.path.join(
 save_directory, self.vocab_files_names["vocab_file"]
 )
 else:
 vocab_file = save_directory

 with open(self.vocab_file, 'rb') as fin:
 proto_str = fin.read()

 with open(vocab_file, "wb") as writer:
 writer.write(proto_str)

 return (vocab_file,)

 def get_prefix_tokens(self):
 prefix_tokens = [self.convert_tokens_to_ids("[gMASK]"), self.convert_tokens_to_ids("<sop>")]
 return prefix_tokens

 def build_single_message(self, role, metadata, message, tokenize=True):
 assert role in ["system", "user", "assistant", "observation"], role
 if tokenize:
 role_tokens = [self.convert_tokens_to_ids(f"<|{role}|>")] + self.tokenizer.encode(f"{metadata}\n",
 disallowed_special=())
 message_tokens = self.tokenizer.encode(message, disallowed_special=())
 tokens = role_tokens + message_tokens
 return tokens
 else:
 return str(f"<|{role}|>{metadata}\n{message}")

 def build_inputs_with_special_tokens(
 self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
 ) -> List[int]:
 """
 Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
 adding special tokens. A BERT sequence has the following format:

 - single sequence: `[CLS] X [SEP]`
 - pair of sequences: `[CLS] A [SEP] B [SEP]`

 Args:
 token_ids_0 (`List[int]`):
 List of IDs to which the special tokens will be added.
 token_ids_1 (`List[int]`, *optional*):
 Optional second list of IDs for sequence pairs.

 Returns:
 `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
 """
 prefix_tokens = self.get_prefix_tokens()
 token_ids_0 = prefix_tokens + token_ids_0
 if token_ids_1 is not None:
 token_ids_0 = token_ids_0 + token_ids_1 + [self.convert_tokens_to_ids("<eos>")]
 return token_ids_0

 def _pad(
 self,
 encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding],
 max_length: Optional[int] = None,
 padding_side: str = "left",
 padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
 pad_to_multiple_of: Optional[int] = None,
 return_attention_mask: Optional[bool] = None,
 ) -> dict:
 """
 Pad encoded inputs (on left/right and up to predefined length or max length in the batch)

 Args:
 encoded_inputs:
 Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).
 max_length: maximum length of the returned list and optionally padding length (see below).
 Will truncate by taking into account the special tokens.
 padding_strategy: PaddingStrategy to use for padding.

 - PaddingStrategy.LONGEST Pad to the longest sequence in the batch
 - PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
 - PaddingStrategy.DO_NOT_PAD: Do not pad
 The tokenizer padding sides are defined in self.padding_side:

 - 'left': pads on the left of the sequences
 - 'right': pads on the right of the sequences
 pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
 This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
 `>= 7.5` (Volta).
 return_attention_mask:
 (optional) Set to False to avoid returning attention mask (default: set to model specifics)
 """
 # Load from model defaults

 required_input = encoded_inputs[self.model_input_names[0]]
 seq_length = len(required_input)

 if padding_strategy == PaddingStrategy.LONGEST:
 max_length = len(required_input)

 if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
 max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

 needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length

 # Initialize attention mask if not present.
 if "attention_mask" not in encoded_inputs:
 encoded_inputs["attention_mask"] = [1] * seq_length

 if "position_ids" not in encoded_inputs:
 encoded_inputs["position_ids"] = list(range(seq_length))

 if needs_to_be_padded:
 difference = max_length - len(required_input)

 if "attention_mask" in encoded_inputs:
 encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"]
 if "position_ids" in encoded_inputs:
 encoded_inputs["position_ids"] = [0] * difference + encoded_inputs["position_ids"]
 encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input

 return encoded_inputs